Oroojeni, Hooman ORCID: 0000-0002-9653-8563 , Al-Rifaie, Mohammad Majid ORCID: 0000-0002-1798-9615 and Nicolaou, Mihalis A (2018) Deep neuroevolution: training deep neural networks for false alarm detection in intensive care units. In: 2018 26th European Signal Processing Conference (EUSIPCO). Rome, Italy. 3-7 Sept. 2018. IEEExplore . Institute of Electrical and Electronics Engineers (IEEE), Piscataway, New Jersey. US, pp. 1157-1161. ISBN 978-9082797015; 978-9082797008; 978-1538637364 ISSN 2219-5491 (Print), 2076-1465 (Online) (doi:https://doi.org/10.23919/EUSIPCO.2018.8552944)

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Abstract

We present a neuroevolution based-approach for training neural networks based on genetic algorithms, as applied to the problem of detecting false alarms in Intensive Care Units (ICU) based on physiological data. Typically, optimisation in neural networks is performed via backpropagation (BP) with stochastic gradient-based learning. Nevertheless, recent works have shown promising results in terms of utilising gradient-free, population-based genetic algorithms, suggesting that in certain cases gradient-based optimisation is not the best approach to follow. In this paper, we empirically show that utilising evolutionary and swarm intelligence algorithms can improve the performance of deep neural networks in problems such as the detection of false alarms in ICU. In more detail, we present results that improve the state-of-the-art accuracy on the corresponding Physionet challenge, while reducing the number of suppressed true alarms by deploying and adapting Dispersive Flies Optimisation (DFO).

Item Type:	Conference Proceedings
Title of Proceedings:	2018 26th European Signal Processing Conference (EUSIPCO). Rome, Italy. 3-7 Sept. 2018
Uncontrolled Keywords:	neuroevolution; dispersive flies optimisation (DF); swarm intelligence
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > Centre for Numerical Modelling & Process Analysis (CNMPA) Faculty of Engineering & Science > Centre for Numerical Modelling & Process Analysis (CNMPA) > Computational Science & Engineering Group (CSEG) Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	23 Jan 2024 23:25
URI:	http://gala.gre.ac.uk/id/eprint/36045

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